1 files changed, 25 insertions, 21 deletions

diff --git a/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
index 04e9fe8..24c61d6 100644
--- a/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
+++ b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
@@ -4,7 +4,6 @@ import jcgp.backend.function.SymbolicRegressionFunctions;
 import jcgp.backend.parameters.BooleanParameter;
 import jcgp.backend.parameters.DoubleParameter;
 import jcgp.backend.parameters.ParameterStatus;
-import jcgp.backend.population.Chromosome;
 import jcgp.backend.population.Population;
 import jcgp.backend.resources.Resources;
 
@@ -20,12 +19,12 @@ import jcgp.backend.resources.Resources;
  * <li>Error threshold: the maximum difference allowed between an
  * evolved output and the equivalent output from the problem data.
  * Outputs within the error threshold will be considered correct.
- * This is only used if HITS is enabled.</li>
+ * This is only used if hits is enabled.</li>
  * <li>Perfection threshold: if the fitness is calculated without
- * using the HITS method, it is a decimal value. A solution is
+ * using the hits method, it is a decimal value. A solution is
  * considered perfect when the difference between its fitness and
  * the maximum possible fitness is within the perfection threshold.</li>
- * <li>HITS-based fitness: increment the fitness by 1 whenever the
+ * <li>Hits-based fitness: increment the fitness by 1 whenever the
  * chromosome output is within the error threshold.</li></ul>
  * 
  * 
@@ -79,7 +78,7 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 			}
 		};
 		
-		hitsBasedFitness = new BooleanParameter(true, "HITS-based fitness");
+		hitsBasedFitness = new BooleanParameter(false, "Hits-based fitness");
 		
 		registerParameters(errorThreshold, perfectionThreshold, hitsBasedFitness);
 	}
@@ -87,15 +86,15 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 	@Override
 	public void evaluate(Population population, Resources resources) {
 		// for every chromosome in the population
-		for (int i = 0; i < resources.populationSize(); i++) {
+		for (int i = 0; i < getResources().populationSize(); i++) {
 			// assume an initial fitness of 0
 			double fitness = 0;
 			// for each test case
 			for (int t = 0; t < testCases.size(); t++) {
-				population.getChromosome(i).setInputs((Object[]) testCases.get(t).getInputs());
+				population.get(i).setInputs((Object[]) testCases.get(t).getInputs());
 				// check each output
-				for (int o = 0; o < resources.outputs(); o++) {
-					Double cgpValue = (Double) population.getChromosome(i).getOutput(o).calculate();
+				for (int o = 0; o < getResources().outputs(); o++) {
+					Double cgpValue = (Double) population.get(i).getOutput(o).calculate();
 					Double dataValue = testCases.get(t).getOutput(o);
 					if (hitsBasedFitness.get()) {
 						if (Math.abs(cgpValue - dataValue) <= errorThreshold.get()) {
@@ -107,11 +106,8 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 				}
 			}
 			// assign the resulting fitness to the respective individual
-			population.getChromosome(i).setFitness(fitness);
+			population.get(i).setFitness(fitness);
 		}
-		
-		// sort population
-		population.sortAscending();
 	}
 	
 	@Override
@@ -130,19 +126,27 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 	}
 
 	@Override
-	public boolean isPerfectSolution(Chromosome fittest) {
+	public int perfectSolutionFound(Population population) {
 		// higher fitness is better
-		return fittest.getFitness() >= maxFitness.get() - perfectionThreshold.get();
+		for (int i = 0; i < getResources().populationSize(); i++) {
+			if (population.get(i).getFitness() >= maxFitness.get() - perfectionThreshold.get()) {
+				return i;
+			}
+		}
+		return -1;
 	}
 	
 	@Override
-	public boolean isImprovement(Chromosome fittest) {
+	public int hasImprovement(Population population) {
 		// higher fitness is better
-		if (fittest.getFitness() > bestFitness.get()) {
-			bestFitness.set(fittest.getFitness());
-			return true;
-		} else {
-			return false;
+		for (int i = 0; i < getResources().populationSize(); i++) {
+			System.out.println("checking for improvement");
+			if (population.get(i).getFitness() > bestFitness.get()) {
+				System.out.println("found a better chr, " + i);
+				bestFitness.set(population.get(i).getFitness());
+				return i;
+			}
 		}
+		return -1;
 	}
 }